Quoc Bao Pham
Global and Planetary Change · 2019–2021
A researcher in the Science corpus: 4 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Global and Planetary Change — 42 citations into it
- Water Science and Technology — 28 citations into it
- Environmental Engineering — 27 citations into it
- Artificial Intelligence — 25 citations into it
- Computational Theory and Mathematics — 13 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
Whom Quoc Bao Pham trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kwok-Wing Chau
- Shie-Yui Liong
- Andy Liaw
- Yonas B. Dibike
- Jianyi Lin
- Robert L. Wilby
- Wenchao Wang
- Zhi-bin He
- Chun-tian Cheng
- Heesung Yoon
Papers from Quoc Bao Pham's perspective
Ordered by how strongly the researchers Quoc Bao Pham trusts endorse each paper. Same corpus, a different order for every researcher.
- The Nature of Statistical Learning Theory
- Model Induction with Support Vector Machines: Introduction and Applications
- Random Forests
- Weighted least squares support vector machines: robustness and sparse approximation (Neurocomputing)
- Particle Swarm Optimization Training Algorithm for ANNs in Stage Prediction of Shing Mun River
- FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES 1
- Artificial intelligence based models for stream-flow forecasting: 2000-2015
- Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree
- LIBSVM: A library for support vector machines
- Classification and Regression by randomForest
Their papers
- Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms (2021) — 168 citations
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020) — 129 citations
- Flood susceptibility modelling using advanced ensemble machine learning models (2020) — 483 citations
- Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling (2019) — 69 citations